GrepSeek: Training Search Agents for Direct Corpus Interaction
Quick summary
arXiv:2605.29307v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) search agents have shown strong promise on knowledge-intensive tasks through iterative reasoning and retrieval. Most existing systems rely on retrievers that return ranked documents from a pre-built index. We explore a complementary paradigm in which the agent treats the corpus as the search environment and finds evidence through executable shell commands. We introduce GrepSeek, an optimized direct corpus interaction (DCI) agent that learns to find, filter, and compose evidence over large text corpora. To stab
Key takeaways
- arXiv:2605.29307v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) search agents have shown strong promise on knowledge-intensive tasks through iterative reasoning and retrieval.
- Most existing systems rely on retrievers that return ranked documents from a pre-built index.
- We explore a complementary paradigm in which the agent treats the corpus as the search environment and finds evidence through executable shell commands.
Why it matters
“GrepSeek: Training Search Agents for Direct Corpus Interaction” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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